Active source localization in wave guides based on machine learning

Hesser, Daniel Frank; Kocur, Georg Karl; Markert, Bernd

doi:10.1016/j.ultras.2020.106144

Cited by 33 publications

(24 citation statements)

References 28 publications

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“…This knowledge was introduced in the CNN network of the data processing pipeline and it helps to define a vector feature for the learning and classification. Hesser et al [ 159 ] investigated the autonomous detection of defects in plate-like metal panels with an ANN that was trained by signals acquired by four commercial sensors (PIC255 from PI Ceramic) with 1 MHz sampling rate and 16 bit resolution ADC. The experimental data set was generated by a free falling ball impact at low velocity (about 1 m/s) that are converted in large amplitude, low phase velocity A 0 mode Lamb waves.…”

Section: Artificial Intelligence and Machine Learningmentioning

confidence: 99%

Ultrasonic Guided-Waves Sensors and Integrated Structural Health Monitoring Systems for Impact Detection and Localization: A Review

Capineri

Bulletti

2021

Sensors

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This review article is focused on the analysis of the state of the art of sensors for guided ultrasonic waves for the detection and localization of impacts for structural health monitoring (SHM). The recent developments in sensor technologies are then reported and discussed through the many references in recent scientific literature. The physical phenomena that are related to impact event and the related main physical quantities are then introduced to discuss their importance in the development of the hardware and software components for SHM systems. An important aspect of the article is the description of the different ultrasonic sensor technologies that are currently present in the literature and what advantages and disadvantages they could bring in relation to the various phenomena investigated. In this context, the analysis of the front-end electronics is deepened, the type of data transmission both in terms of wired and wireless technology and of online and offline signal processing. The integration aspects of sensors for the creation of networks with autonomous nodes with the possibility of powering through energy harvesting devices and the embedded processing capacity is also studied. Finally, the emerging sector of processing techniques using deep learning and artificial intelligence concludes the review by indicating the potential for the detection and autonomous characterization of the impacts.

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Section: Artificial Intelligence and Machine Learningmentioning

confidence: 99%

Ultrasonic Guided-Waves Sensors and Integrated Structural Health Monitoring Systems for Impact Detection and Localization: A Review

Capineri

Bulletti

2021

Sensors

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“…4) Adopting simulated signals in the construction of ML models and experimental signals for validation. This strategy is important since it avoids expensive measurement campaigns, and provides an effective means for creating datasets [37], [42]. In summary, this work is comprised of a method to detect and estimate corrosion-like defects in aluminium plates, based on using SH guided waves.…”

Section: A Contributionsmentioning

confidence: 99%

“…DL is also explored with Lamb waves, in which, wavelet coefficients are extracted and used as inputs of the models [41]. Moreover, Hesser et al [42] have proposed an active source localization to predict the impact position of a steel ball in an aluminium plate, using numerical simulations to train and experimental data to validate ML models. Finally, Sun et al [43] very recently proposed a damage classification in plates, using DL architectures jointly with SH guided waves.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Corrosion-Like Defect Estimation With Shear-Horizontal Guided Waves Improved by Mode Separation

et al. 2021

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Shear Horizontal (SH) guided waves have been extensively used to estimate and detect defects in structures like plates and pipes. Depending on the frequency and plate thickness, more than one guided-wave mode propagates, which renders signal interpretation complicated due to mode mixing and complex behavior of each individual mode interacting with defects. This paper investigates the use of machine learning models to analyse the two lowest order SH guided modes, for quantitative size estimation and detection of corrosion-like defects in aluminium plates. The main contribution of the present work is to show that mode separation through machine learning improves the effectiveness of predictive models. Numerical simulations have been performed to generate time series for creating the estimators, while experimental data have been used to validate them. We show that a full mode separation scheme decreased the error rate of the final model by 30% and 67% in defect size estimation and detection respectively.

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“…Hesser et al used simulated impact damage to train a multi-layer perceptron to localize the impact positions in a plate [16]. Todd et al employed a Bayesian neural network surrogate model with variational inference, learning latent damage features to monitor length of unexpected gaps between the lock wall and the quoin block in cargo ships [17].…”

Section: Introductionmentioning

confidence: 99%

Defect detection in guided wave signals using nonlinear autoregressive exogenous method

Wang

Zhang

Shen

et al. 2021

Structural Health Monitoring

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To perform long-term structural health monitoring, a method based on a nonlinear autoregressive exogenous network is used to learn the features present in signals acquired from a pristine structure. When a subsequent measured signal is input to the trained nonlinear autoregressive exogenous network, the output is a prediction of the equivalent signal from a pristine structure. The residual when the pristine predicted signal is subtracted from the measured signal is used for defect detection and localization. A methodology of how to train, test and assess a nonlinear autoregressive exogenous network for guided wave signals is introduced and applied to experimental data obtained over a period of 8 years from a sparse array of guided wave sensors deployed on a steel storage tank. A separate nonlinear autoregressive exogenous model is trained for each sensor pair in the array using data captured in 2012. The method is first tested using data from a single pair of sensors. Defect signals are synthesized by superposing simulated responses from defects onto later experimental signals obtained from the real structure. The test results for the nonlinear autoregressive exogenous method show better detection performance than those from the optimal baseline selection method, in terms of receiver operating characteristic curves. The detection performance of the nonlinear autoregressive exogenous method is further assessed on signals from the whole sensor array, again with simulated defect responses superposed. It is shown that good detection and localization performance can be achieved by combining the nonlinear autoregressive exogenous residual signals from different sensor pairs. The nonlinear autoregressive exogenous method is tested on experimental data acquired at intervals over the following 7 years as the condition of the tank naturally degrades. Indications from localized corrosion are observed. Finally, an artificial localized anomaly is added to the tank and is visible at the correct location in the image formed using the nonlinear autoregressive exogenous method.

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Active source localization in wave guides based on machine learning

Cited by 33 publications

References 28 publications

Ultrasonic Guided-Waves Sensors and Integrated Structural Health Monitoring Systems for Impact Detection and Localization: A Review

Ultrasonic Guided-Waves Sensors and Integrated Structural Health Monitoring Systems for Impact Detection and Localization: A Review

Machine Learning-Based Corrosion-Like Defect Estimation With Shear-Horizontal Guided Waves Improved by Mode Separation

Defect detection in guided wave signals using nonlinear autoregressive exogenous method

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